The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Comprehensive Algorithm for Quantitative Real-Time Polymerase Chain Reaction
Open accessAbstract
Quantitative real-time polymerase chain reactions (qRT-PCR) have become the method of choice for rapid, sensitive, quantitative comparison of RNA transcript abundance. Useful data from this method depend on fitting data to theoretical curves that allow computation of mRNA levels. Calculating accurate mRNA levels requires important parameters such as reaction efficiency and the fractional cycle number at threshold (CT) to be used; however, many algorithms currently in use estimate these important parameters. Here we describe an objective method for quantifying qRT-PCR results using calculations based on the kinetics of individual PCR reactions without the need of the standard curve, independent of any assumptions or subjective judgments which allow direct calculation of efficiency and CT. We use a four-parameter logistic model to fit the raw fluorescence data as a function of PCR cycles to identify the exponential phase of the reaction. Next, we use a three-parameter simple exponent model to fit the exponential phase using an iterative nonlinear regression algorithm. Within the exponential portion of the curve, our technique automatically identifies candidate regression values using the P-value of regression and then uses a weighted average to compute a final efficiency for quantification. For CT determination, we chose the first positive second derivative maximum from the logistic model. This algorithm provides an objective and noise-resistant method for quantification of qRT-PCR results that is independent of the specific equipment used to perform PCR reactions.
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Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the 2−ΔΔCT Method
Livak, K. J., & Schmittgen, T. D. (2001) · Methods · doi:10.1006/meth.2001.1262 · Cited by 180,860
Mutation of the mouse klotho gene leads to a syndrome resembling ageing
Kuro-o, M., Matsumura, Y., Aizawa, H., Kawaguchi, H., Suga, T., Utsugi, T., Ohyama, Y., Kurabayashi, M., Kaname, T., Kume, E., et al. (1997) · Nature · doi:10.1038/36285 · Cited by 3,837
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Amplification efficiency: linking baseline and bias in the analysis of quantitative PCR data
Ruijter, J. M., Ramakers, C., Hoogaars, W. M. H., Karlen, Y., Bakker, O., van den Hoff, M. J. B., & Moorman, A. F. M. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp045 · Cited by 3,043
Zhuang, X., Xiang, X., Grizzle, W., Sun, D., Zhang, S., Axtell, R. C., Ju, S., Mu, J., Zhang, L., Steinman, L., et al. (2011) · Molecular Therapy · doi:10.1038/mt.2011.164 · Cited by 1,356
Harter, J., Krause, H.-M., Schuettler, S., Ruser, R., Fromme, M., Scholten, T., Kappler, A., & Behrens, S. (2014) · The ISME Journal · doi:10.1038/ismej.2013.160 · Cited by 679
Montini, E., Cesana, D., Schmidt, M., Sanvito, F., Bartholomae, C. C., Ranzani, M., Benedicenti, F., Sergi, L. S., Ambrosi, A., Ponzoni, M., et al. (2009) · Journal of Clinical Investigation · doi:10.1172/jci37630 · Cited by 571
Evolution of human-specific neural SRGAP2 genes by incomplete segmental duplication
Dennis, M. Y., Nuttle, X., Sudmant, P. H., Antonacci, F., Graves, T. A., Nefedov, M., Rosenfeld, J. A., Sajjadian, S., Malig, M., Kotkiewicz, H., et al. (2012) · Cell · doi:10.1016/j.cell.2012.03.033 · Cited by 407
Kirby, E. D., Geraghty, A. C., Ubuka, T., Bentley, G. E., & Kaufer, D. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0901176106 · Cited by 379
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Bar‐Klein, G., Cacheaux, L. P., Kamintsky, L., Prager, O., Weissberg, I., Schoknecht, K., Cheng, P., Kim, S. Y., Wood, L., Heinemann, U., et al. (2014) · Annals of Neurology · doi:10.1002/ana.24147 · Cited by 270
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Borrelli, L., Aceto, S., Agnisola, C., De Paolo, S., Dipineto, L., Stilling, R. M., Dinan, T. G., Cryan, J. F., Menna, L. F., & Fioretti, A. (2016) · Scientific Reports · doi:10.1038/srep30046 · Cited by 225
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Geraghty, A. C., Muroy, S. E., Zhao, S., Bentley, G. E., Kriegsfeld, L. J., & Kaufer, D. (2015) · eLife · doi:10.7554/elife.04316 · Cited by 87
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Farr, J. N., Roforth, M. M., Fujita, K., Nicks, K. M., Cunningham, J. M., Atkinson, E. J., Therneau, T. M., McCready, L. K., Peterson, J. M., Drake, M. T., et al. (2015) · PLOS ONE · doi:10.1371/journal.pone.0138347 · Cited by 75
Rossi, M., Demidov, O. N., Anderson, C. W., Appella, E., & Mazur, S. J. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn888 · Cited by 57
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Metadata
- Authors
- Zhao, S., & Fernald, R. D.
- Venue
- Journal of Computational Biology
- Publisher
- SAGE Publications
- Published
- 2005-10
- Volume
- 12
- Issue
- 8
- Pages
- 1047-1064
- ISSN
- 1066-5277 (ISSN-L)
- 1557-8666
- OpenAlex
- W1975165380
- Cited by
- 1,303
- References
- 2
- FWCI
- 4.38
- Type
- journal article
- Language
- en
- Open access
- green
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